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For virtual instructor-led Kafka Official Class, please reach out to us at operations🤍datacouch.io We are an official training delivery partner of Confluent.. We conduct corporate trainings on various topics including Confluent Kafka Developer, Confluent Kafka Administration, Confluent Kafka Real Time Streaming using KSQL & KStreams and Confluent Kafka Advanced Optimization. Our instructors are well qualified and vetted by Confluent for delivering such courses. In this video, you will see a comparison between Hive and Spark SQL. Let’s come together in Joining our strong 3700+ 𝐦𝐞𝐦𝐛𝐞𝐫𝐬 community where we impart our knowledge regularly on Data, ML, AI, and many more technologies: 🤍 𝐒𝐭𝐚𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐮𝐬! 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 𝐌𝐞𝐝𝐢𝐮𝐦: 🤍 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬: 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Hive #SparkSQL #HivevsSparkSQL #Difference #Technology #SparkSQLvsHive #Hadoop #BigData #Sql #DataCouch
*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥Post Graduate Program In Data Engineering: 🤍 🔥Big Data Engineer Masters Program (Discount Code - YTBE15): 🤍 Hadoop and Spark are the two most popular big data technologies used for solving significant big data challenges. In this video, you will learn which of them is faster based on performance. You will know how expensive they are and which among them is fault-tolerant. You will get an idea about how Hadoop and Spark process data, and how easy they are for usage. You will look at the different languages they support and what's their scalability. Finally, you will understand their security features, which of them has the edge over machine learning. Now, let's get started with learning Hadoop vs. Spark. We will differentiate based on below categories 1. Performance 00:52 2. Cost 01:40 3. Fault Tolerance 02:31 4. Data Processing 03:06 5. Ease of Use 04:03 6. Language Support 04:52 7. Scalability 05:55 8. Security 06:38 9. Machine Learning 08:02 10. Scheduler 08:56 To learn more about Hadoop, subscribe to our YouTube channel: 🤍 To access the slides, click here: 🤍 Watch more videos on HadoopTraining: 🤍 #HadoopvsSpark #HadoopAndSpark #HadoopAndSparkDifference #DifferenceBetweenHadoopAndSpark #WhatIsHadoop #WhatIsSpark #LearnHadoop #HadoopTraining #SparkTraining #HadoopCertification #SimplilearnHadoop #Simplilearn 🔥 Enroll for FREE Big Data Hadoop Spark Course & Get your Completion Certificate: 🤍 ➡️ About Post Graduate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features Post Graduate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real-Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥Post Graduate Program In Data Engineering: 🤍 🔥Big Data Engineer Masters Program (Discount Code - YTBE15): 🤍 Hadoop is a famous Big Data framework; this video on Hadoop will acquaint you with the term Big Data and help you understand the importance of Hadoop. Here, you will also learn about the three main components of Hadoop, namely, HDFS, MapReduce, and YARN. In the end, we will have a quiz on Hadoop. Hadoop is a framework that manages Big Data storage in a distributed way and processes it parallelly. Now, let's get started and learn all about Hadoop. Don't forget to take the quiz at 05:11! To learn more about Hadoop, subscribe to our YouTube channel: 🤍 Watch more videos on HadoopTraining: 🤍 #WhatIsHadoop #Hadoop #HadoopExplained #IntroductionToHadoop #HadoopTutorial #Simplilearn Big Data #SimplilearnHadoop #simplilearn ➡️ Post Graduate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, masterclasses, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features - Professional Certificate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉Learn More at: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
= Apache Spark SQL With Apache Hive Apache Spark Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Apache Hive Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Hadoop Installation - 🤍 Hive Installation - 🤍 Spark Installation - 🤍 Video Playlist - Hadoop in Tamil - 🤍 Hadoop in English - 🤍 Spark in Tamil - 🤍 Spark in English - 🤍 Hive in Tamil - 🤍 Hive in English - 🤍 Batch vs Stream processing Tamil - 🤍 Batch vs Stream processing English - 🤍 NOSQL in English - 🤍 NOSQL in Tamil - 🤍 Scala in Tamil : 🤍 Scala in English: 🤍 Email: atozknowledge.com🤍gmail.com LinkedIn : 🤍 Instagram: 🤍 YouTube channel link 🤍youtube.com/atozknowledgevideos Website 🤍 Technology in Tamil & English #apachespark #apachehive #sparksql
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*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥Post Graduate Program In Data Engineering: 🤍 🔥Big Data Engineer Masters Program (Discount Code - YTBE15): 🤍 In this short video, you will see a comparison between Apache Hive and Apache Pig. You will see various comparisons such as why Hive & Pig, what is Hive & Pig, HiveQL & Pig Latin, data models, execution modes, features and commands. Apache MapReduce mainly works on Java codes, to make data processing easier Hive and Pig were introduced. Hive and Pig work on SQL like queries; this makes processing and analyzing data way more easier compared to MapReduce. Now, let us get started and understand the differences between Hive and Pig. Below topics are explained in this "Hive vs Pig" video: 1. Need for Hive & Pig 2. What is Hive & Pig 3. HiveQL & Pig Latin 4. Data models 5. Execution modes 6. Features 7. Commands To learn more about Hadoop, subscribe to our YouTube channel: 🤍 To access the slides, click here: 🤍 Watch more videos on Hadoop training: 🤍 #HiveVsPig #HiveAndPig #HadoopHive #Hive #HadoopPig #PigTutorial #LearnHadoop #HadoopTraining #HadoopCertification #SimplilearnHadoop #Simplilearn 🔥Free Big Data Hadoop Spark Developer Course: 🤍 ➡️ About Post Graduate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features Post Graduate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real-Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
00:00 - What is difference between Hadoop and Hive? 00:38 - Is Hadoop OLTP or OLAP? 01:15 - Is hive a Hadoop? 01:44 - Can hive run without Hadoop? 02:18 - Does hive require Hadoop? Laura S. Harris (2021, January 18.) What is difference between Hadoop and Hive? AskAbout.video/articles/What-is-difference-between-Hadoop-and-Hive-210767 Our main goal is creating educational content. The topic of this video has been processed in the spirit of this goal. If required by education, we may also present a detail of the topic that may be objectionable to some people.
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Кратко про Хадуп и Спарк. Экосистема Hadoop. Экосистема Spark. Инфраструктура Hadoop и Spark.
Learn more about Apache Spark → 🤍 Check out IBM Analytics Engine → 🤍 Unboxing the IBM POWER E1080 Server → 🤍 Do you have a big data problem? Too much data to process or queries that are too costly to run in a reasonable amount of time? Spare your wallet and stress levels! David Adeyemi introduces Apache Spark. It may save you a hardware upgrade or testing your patience waiting for a SQL query to finish. Get started for free on IBM Cloud → 🤍 Subscribe to see more videos like this in the future → 🤍
This video is part of the Spark learning Series. Spark provides different methods to optimize the performance of queries. So As part of this video, we are covering the following What is Partitioning How does partitioning help to improve performance What is Bucketing How does bucketing helps to improve performance Difference between Partitioning and Bucketing How Spark's performance is impacted by Dynamic Partition Pruning Here are a few Links useful for you Git Repo: 🤍 Spark Interview Questions: 🤍 Spark performance tuning: If you are interested to join our community. Please join the following groups Telegram: 🤍 Whatsapp: 🤍 You can drop me an email for any queries at aforalgo🤍gmail.com #apachespark #sparktutorial #bigdata #spark #hadoop #spark3
Comparison of two popular SQL on Hadoop technologies - Apache Hive and Impala. In the video, we will review some of the architectural design differences between the two and discuss the pro and cons of Cloudera Impala vs Hive. And finally explore scenarios where you can leverage the strengths of Hive and Impala and use it together in hybrid scenarios.
🔥Intellipaat Big Data Hadoop Course: 🤍 In this video on Hadoop vs Spark you will understand about the top Big Data solutions used in the IT industry, and which one should you use for better performance. So in this Hadoop MapReduce vs Spark comparison some important parameters have been taken into consideration to tell you the difference between Hadoop and Spark also which one is preferred over the other in certain aspects in detail. #HadoopvsSpark #ApacheSparkvsHadoop #SparkvsHadoop #DifferenceBetweenSparkandHadoop #intellipaat 📌 Do subscribe to Intellipaat channel & get regular updates on videos: 🤍 📝Following topics are covered in this Hadoop vs Spark comparison tutorial: 01:25 - What is Hadoop? 03:40 - Hadoop Ecosystem 07:15 - What is Spark? 08:25 - Spark Ecosystem 09:27 - Apache Spark components 12:15 - Hadoop vs Spark 19:04 - Job trends and Salaries 20:27 - Which is better to choose? 23:33 - Quiz 📰Interested to learn big data hadoop still more? Please check similar hadoop blogs here: 🤍 If you’ve enjoyed this Hadoop vs Spark which is better video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. What do you think which one of them is better among Hadoop vs Spark according to you? Tell us in the comment section below. Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Course ware 6. Life time free Course Upgrade Why Hadoop is important Big data hadoop is one of the best technological advances that is finding increased applications for big data and in a lot of industry domains. Data is being generated hugely in each and every industry domain and to process and distribute effectively hadoop is being deployed everywhere and in every industry. Why Spark is important Today there is a widespread deployment of Big Data. With each passing day the requirements of enterprises increases and therefore there is a need for a faster and more efficient form of processing data. Most of the data is in unstructured format and it is coming in thick and fast as streaming data.Apache Spark is seeing widespread demand with enterprises finding it increasingly difficult to hire the right professionals to take on increasingly challenging roles in real world scenarios. It is a fact that today the Apache Spark community is one of the fastest Big Data communities with over 750 contributors from over 200 companies worldwide. For more Information: Please write us to sales🤍intellipaat.com, or call us at: +91- 7847955955 Website: 🤍 Facebook: 🤍 LinkedIn: 🤍 Telegram: 🤍 Instagram: 🤍 Twitter: 🤍
Spark Architecture Part 1 What is scaling? Vertical vs horzontal scaling? 🤍 what is VCPU? CPU - Core - Threads 🤍 Spark Architecture Part 1: Spark Vs Hadoop MR spark vs mapreduce 🤍 Spark Architecture Part2 : master slave architecture , Single node cluster , multi note cluster 🤍 Spark Architecture Part 3 : sparksession vs sparkcontext 🤍 Spark Architecture Part 4 : Spark job to stage and stage to task spark job spark stages 🤍 Spark Architecture Part 5 : Spark narrow & wide transformations 🤍 Spark Architecture Part 6 : pyspark word count program example 🤍 hadoop vs spark,spark vs hadoop mapreduce,spark vs hadoop,spark vs hadoop difference,spark vs mapreduce,apache spark,map reduce,apache spark vs hadoop,spark vs map reduce,spark,spark tutorial,apache spark vs mapreduce,hadoop spark,mapreduce vs spark,spark vs hadoop map reduce,what is spark,hadoop mapreduce vs spark,spark architecture,spark hadoop,hadoop map reduce,apache spark tutorial,spark vs hadoop edureka,spark sql
A common question that organizations looking to adopt a big data strategy struggle with is - which solution might be a better fit, Hadoop vs. Spark, or both? To help answer that question, here’s a comparative look at these two big data frameworks. You can learn more about Hadoop and Spark in the blog below 🤍
This hangout is to cover difference between different execution engines available in Hadoop and Spark clusters
🔥 Edureka Apache Spark Training: 🤍 🔥 Edureka Hadoop Training: 🤍 This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at: 1. Introduction to Hadoop 2. Introduction to Apache Spark 3. Spark vs Hadoop - Performance Ease of Use Cost Data Processing Fault tolerance Security 4. Hadoop Use-cases 5. Spark Use-cases Edureka Big Data Training and Certifications 🔵 Edureka Hadoop Training: 🤍 🔵 Edureka Spark Training: 🤍 🔵 Edureka Kafka Training: 🤍 🔵 Edureka Cassandra Training: 🤍 🔵 Edureka Talend Training: 🤍 🔵 Edureka Hadoop Administration Training: 🤍 Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Hadoop playlist here: 🤍 & Spark Playlist here: 🤍 - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, Please write back to us at sales🤍edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favourite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app... I've take two courses, and I'm taking two more.”
BigData is trending. Organizations are shifting to BigData for their huge data storage and processing related issues. Videos on this channel will showcase performance and analysis of different Bigdata frameworks, tools and technologies. Also, we will discuss about the internals and architecture of mapreduce, spark and other frameworks. Please subscribe to this channel for more information. You can check other videos of this channel using the link mentioned below : 🤍
cd Desktop/ scp -i TheKey.pem -r /Users/NY/Desktop/sales_records_1_ADTA5240.csv root🤍ec2-54-172-16-247.compute-1.amazonaws.com:/home/w205 ssh -i TheKey.pem root🤍ec2-18-204-195-189.compute-1.amazonaws.com mount -t ext4 /dev/xvdf /data /root/start-hadoop.sh /data/start_postgres.sh /data/start_metastore.sh su - w205 hdfs dfs -mkdir /user/w205/project hdfs dfs -put sales_records_1_ADTA5240.csv /user/w205/project ### PROGRAM:HIVE hive CREATE EXTERNAL TABLE IF NOT EXISTS sales_table_hive ( RowID string, OrderID string, OrderDate string, ShipDate string, ShipMode string, CustomerID string, CustomerName string, Segment string, Country string, City string, State string, Postalcode string, Region string, ProductID string, Category string, SubCategory string, ProductName string, Sales string, Quantity string, Discount string, Profit string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE LOCATION '/user/w205/project/'; LOAD DATA INPATH "/user/w205/project/sales_records_1_ADTA5240.csv" INTO TABLE sales_table_hive; SELECT State, COUNT (CustomerID) AS total_customers_state FROM sales_table_hive GROUP BY State ORDER BY total_customers_state DESC LIMIT 5; SELECT Postalcode, SUM (Sales) AS total_sales_postalcode FROM sales_table_hive GROUP BY Postalcode ORDER BY total_sales_postalcode DESC LIMIT 10; ### PROGRAM:spark-sql CREATE EXTERNAL TABLE IF NOT EXISTS sales_table_spark ( RowID varchar(500), OrderID varchar(500), OrderDate varchar(500), ShipDate varchar(500), ShipMode varchar(500), CustomerID varchar(500), CustomerName varchar(500), Segment varchar(500), Country varchar(500), City varchar(500), State varchar(500), Postalcode varchar(500), Region varchar(500), ProductID varchar(500), Category varchar(500), SubCategory varchar(500), ProductName varchar(500), Sales varchar(500), Quantity varchar(500), Discount varchar(500), Profit varchar(500) ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE LOCATION '/user/w205/project/'; LOAD DATA INPATH "/user/w205/project/sales_records_1_ADTA5240.csv" INTO TABLE sales_table_spark; SELECT State, COUNT(CustomerID) FROM sales_table_spark GROUP BY State ORDER BY COUNT(CustomerID) DESC LIMIT 5; SELECT Postalcode, SUM (Sales) FROM sales_table_spark GROUP BY Postalcode ORDER BY SUM (Sales) DESC LIMIT 10; SELECT State, COUNT (DISTINCT CustomerID) AS total_customers_state FROM sales_table_hive GROUP BY State ORDER BY total_customers_state DESC LIMIT 5; SELECT State, COUNT (DISTINCT CustomerID) FROM sales_table_spark GROUP BY State ORDER BY COUNT(DISTINCT CustomerID) DESC LIMIT 5;
ATTENTION DATA SCIENCE ASPIRANTS: Click Below Link to Download Proven 90-Day Roadmap to become a Data Scientist in 90 Days 🤍 Apache Hive Beginner's Guide : 🤍 Apache Hive Courses : 🤍 In this video, you will get a quick overview of Apache Hive, one of the most popular data warehouse components on the big data landscape. It’s mainly used to complement the Hadoop file system with its interface. Hive was originally developed by Facebook and is now maintained as Apache hive by Apache software foundation. It is used and developed by biggies such as Netflix and Amazon as well. Why was Hive Developed = The Hadoop ecosystem is not just scalable but also cost effective when it comes to processing large volumes of data. It is also a fairly new framework that packs a lot of punch. However, organizations with traditional data warehouses are based on SQL with users and developers that rely on SQL queries for extracting data. It makes getting used to the Hadoop ecosystem an uphill task. And that is exactly why hive was developed. Hive provides SQL intellect, so that users can write SQL like queries called HQL or hive query language to extract the data from Hadoop. These SQL likes queries will be converted into map reduce jobs by the Hive component and that is how it talks to Hadoop ecosystem and HDFS file system. How and when Hive can be used? = Hive can be used for OLAP (online analytic) processing It is scalable, fast and flexible It is a great platform for the SQL users to write SQL like queries to interact with the large datasets that reside on HDFS filesystem Here is what Hive cannot be used for: It is not a relational database It cannot be used for OLTP (online transaction) processing It cannot be used for real time updates or queries It cannot be used for scenarios where low latency data retrieval is expected, because there is a latency in converting the HIVE scripts into MAP REDUCE scripts by Hive Some of the finest features of Hive It supports different file formats like sequence file, text file, avro file format, ORC file, RC file Metadata gets stored in RDBMS like derby database Hive provides lot of compression techniques, queries on the compressed data such as SNAPPY compression, gzip compression Users can write SQL like queries that hive converts into mapreduce or tez or spark jobs to query against hadoop datasets Users can plugin mapreduce scripts into the hive queries using UDF user defined functions Specialized joins are available that help to improve the query performance If you don’t understand any of the above terms, that is fine. We will look into the above features in detail in our upcoming videos.
What is Apache spark? And how does it fit into Big Data? How is it related to hadoop? We'll look at the architecture of spark, learn some of the key components, see how it related to other big data tools like hadoop. ⏯RELATED VIDEOS⏯ Building a Data Pipeline: 🤍 Data Podcast ►► 🤍 Website ►► 🤍 🎓Data courses (Not Produced by nullQueries)🎓 Azure Data Engineering: 🤍 DE Essentials, hands on: 🤍 📷VIDEO GEAR📷 Programming Mouse: 🤍 Lighting: 🤍 RGB light: 🤍 USB Microphone: 🤍 Mixer: 🤍 XLR Microphone: 🤍 💻VIDEO SOFTWARE💻 music/stock: 🤍 For business inquiries please contact nullQueries🤍gmail.com Some of the links in this description are affiliate links and support the channel. Thanks for the support! 00:00 Intro 00:25 History 00:44 Goals 00:58 Architecture 02:22 Libraries 02:57 Platforms 02:57 Comparisons
On clusters of affordable hardware, Hadoop is an open-source software platform for data storage and application execution. It offers immense data storage, huge processing power, and the ability to conduct essentially infinite concurrent tasks or processes. There are various things that make Hadoop essential, right from flexibility to its scalability. But, as with everything in this rapidly changing technological world, Hadoop also has a shelf life. The question is, has it crossed its shelf life, or is it still just as important as it used to be a decade ago? Watch this short video to find out. #SCALER #hadoop #bigdata #dataengineering #shorts
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In this video, we will understand the differences between Spark and Tez Let’s come together in Joining our strong 3500+ 𝐦𝐞𝐦𝐛𝐞𝐫𝐬 community where we impart our knowledge regularly on Data, ML, AI, and many more technologies: 🤍 𝐒𝐭𝐚𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐮𝐬! 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 𝐌𝐞𝐝𝐢𝐮𝐦: 🤍 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬: 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Spark #Tez #SparkvsTez #TezvsSpark #Difference #Comparison #DataCouch
For virtual instructor-led Kafka Official Class, please reach out to us at operations🤍datacouch.io We are an official training delivery partner of Confluent.. We conduct corporate trainings on various topics including Confluent Kafka Developer, Confluent Kafka Administration, Confluent Kafka Real Time Streaming using KSQL & KStreams and Confluent Kafka Advanced Optimization. Our instructors are well qualified and vetted by Confluent for delivering such courses. Let's explore the comparison of two popular SQL technologies based on Hadoop i.e. Hive and Impala! Let’s come together in Joining our strong 3700+ 𝐦𝐞𝐦𝐛𝐞𝐫𝐬 community where we impart our knowledge regularly on Data, ML, AI, and many more technologies: 🤍 𝐒𝐭𝐚𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐮𝐬! 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬: 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Hive #Impala #HivevsImpala #Difference #Technology #Hadoop #BigData #sql #DataCouch
One of the core, vital stepping stones between a Data Lake and data consumers in the business is the ability to view the contents of a lake as a logical structure - Hive enables this and is the mechanism for presenting a data model out to BI tools such as Power BI and Tableau... but we keep meeting people who never use it! This week, Simon goes back to the basics to give a quick intro tour to registering tables with Hive, then shows some of his favourite tricks to loop through an entire database, or register tables programmatically. As always, don't forget to like and subscribe, and don't forget to drop by our website if you need any help getting started on your spark journey - 🤍
Hive and Spark Integration Tutorial | Hadoop Tutorial for Beginners 2018 | Hadoop Training Videos #1 🤍 Hello and welcome to Big Data and Hadoop tutorial series powered by ACADGILD. Let’s start with the 1st part of the Hadoop tutorial series. In this Hadoop tutorial video, we will take you through Hive and spark integration. Hive uses metastore to keep information about tables. The greatest advantages of metastore is it shares information with other components in the Hadoop ecosystem and also spark. Spark on other hand has its own optimized SQL execution engine to give faster results. Spark SQL engine can sit on HDFS or any other file system but in today session we use Hive datastore How does spark connect with Hive? Does Spark calls Hive internally? No, spark only reads the metadata from the hive and executes the query within the Spark engine. We will be using HiveContext class, to get access to metastore_db and all your Hive Meta_Data, which can clearly explain what type of data you have, literally every detail about the table and its data. Where do you have the data, serialization and deserializations, columns, datatypes and That is enough for the spark to understand the data. Overall, Spark only needs metastore to execute the queries that you asked for its execution engine. Hive is slower than Spark as it uses MapReduce. So, there is no point in going back to hive and ask to run the queries in Hive. Go through complete video and learn how to work on Hive and Spark integration and become a data scientist by enrolling the course. Please like and share the video and kindly give your feedbacks and subscribe the channel for more tutorial videos. For more updates on courses and tips follow us on: Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍
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As part of our spark tutorial series, we are going to explain spark concepts in very simple and crisp way. We will different topics under spark, like spark , spark sql, datasets, rdd , accumulator, broadcast variables, jins with rdd etc. We hope this will be useful for your learning experienece. As part of this video we are covering what is difference between avro and parquet and orc format. orc vs parquet 2018 presto orc vs parquet athena orc vs parquet difference between orc and parquet format spark orc vs parquet performance parquet files vs orc files orc vs parquet vs avro gzip vs orc parquet file format Please subscribe to our channel. Here is link to other spark interview questions 🤍 Here is link to other Hadoop interview questions 🤍 #spark #hadoop #bigdata #orc #parquet #orcvsparquet
This lecture is all about Apache Tez which is data processing framework designed to handle Big Data on Hadoop by creating complex DAG instead of MapReduce approach. We have also seen why Tez is way more efficient than MapReduce and compared the query execution times between Tez and MapReduce. Commands for this lecture: -on hive shell set hive.execution.engine=tez; select movie_id, count(rating) from default.ratings group by movie_id order by movie_id; set hive.execution.engine=mr; - In the previous lecture we have seen Hadoop resource management component i.e. YARN (Yet Another Resource Negotiator) where we have discussed what is Hadoop YARN, significance of YARN, architecture of YARN and the process flow. - HDP Sandbox Installation links: Oracle VM Virtualbox: 🤍 HDP Sandbox link: 🤍 HDP Sandbox installation guide: 🤍 - Also check out similar informative videos in the field of cloud computing: What is Big Data: 🤍 How Cloud Computing changed the world: 🤍 What is Cloud? 🤍 Top 10 facts about Cloud Computing that will blow your mind! 🤍 Audience This tutorial is made for professionals who are willing to learn the basics of Big Data Analytics using Hadoop Ecosystem and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course. Prerequisites Before you start proceeding with this course, I am assuming that you have some basic knowledge to Core Java, database concepts, and any of the Linux operating system flavors. - Check out our full course topic wise playlist on some of the most popular technologies: SQL Full Course Playlist- 🤍 PYTHON Full Course Playlist- 🤍 Data Warehouse Playlist- 🤍 Unix Shell Scripting Full Course Playlist- 🤍 Don't forget to like and follow us on our social media accounts which are linked below. Facebook- 🤍 Instagram- 🤍 Twitter- 🤍 Tumblr- ampcode.tumblr.com - Channel Description- AmpCode provides you e-learning platform with a mission of making education accessible to every student. AmpCode will provide you tutorials, full courses of some of the best technologies in the world today.By subscribing to this channel, you will never miss out on high quality videos on trending topics in the areas of Big Data & Hadoop, DevOps, Machine Learning, Artificial Intelligence, Angular, Data Science, Apache Spark, Python, Selenium, Tableau, AWS , Digital Marketing and many more. #bigdata #datascience #dataanalytics #datascientist #hadoop #hdfs #hdp #mongodb #cassandra #hbase #nosqldatabase #nosql #pyspark #spark #presto #hadooptutorial #hadooptraining
spark series As part of our spark tutorial series, we are going to explain spark concepts in very simple and crisp way. We will different topics under spark, like spark , spark sql, datasets, rdd , accumulator, broadcast variables, jins with rdd etc. We hope this will be useful for your learning experienece. As part of this video we are covering what is difference between Hive bucketing and partitioning. and when to use bucketing, when to use partitions Please subscribe to our channel. Here is link to other spark interview questions 🤍 Here is link to other Hadoop interview questions 🤍
*Note: 1+ Years of Work Experience Recommended to Sign up for Below Programs⬇️ 🔥Post Graduate Program In Data Engineering: 🤍 🔥Big Data Engineer Masters Program (Discount Code - YTBE15): 🤍 This Simplilearn video on Hive tutorial speaks about Hive architecture and all about Apache Hive. You will learn what is Hive In Hadoop, data flow in Hive, Hive vs RDBMS, Hive features, etc. Finally, you will see a hands-on demo session on HiveQL commands. So, let's get started with this Hive Tutorial For Beginners! Below topics are explained in this Hive tutorial: 1. History of Hive 00:00 2. What is Hive? 01:57 3. Architecture of Hive 02:23 4. Data flow in Hive 05:33 5. Hive data modeling 07:07 6. Hive data types 08:45 7. Different modes of Hive 11:47 8. Difference between Hive and RDBMS 13:05 9. Features of Hive 16:28 10. Demo on HiveQL 18:04 To learn more about Hadoop, subscribe to our YouTube channel: 🤍 To access slides, click here: 🤍 Watch more videos on Hadoop training: 🤍 #HiveTutorial #HadoopHive #Hadoop #HBaseArchitecture #HadoopTutorialForBeginners #LearnHadoop #HadoopTraining #HadoopCertification #SimplilearnHadoop #Simplilearn 🔥 Enroll for FREE Big Data Hadoop Spark Course & Get your Completion Certificate: 🤍 ➡️ About Post Graduate Program In Data Engineering This Data Engineering course is ideal for professionals, covering critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data on AWS, and Azure cloud infrastructures. This program is delivered via live sessions, industry projects, IBM hackathons, and Ask Me Anything sessions. ✅ Key Features Post Graduate Program Certificate and Alumni Association membership - Exclusive Master Classes and Ask me Anything sessions by IBM - 8X higher live interaction in live Data Engineering online classes by industry experts - Capstone from 3 domains and 14+ Projects with Industry datasets from YouTube, Glassdoor, Facebook etc. - Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Skills Covered - Real-Time Data Processing - Data Pipelining - Big Data Analytics - Data Visualization - Provisioning data storage services - Apache Hadoop - Ingesting Streaming and Batch Data - Transforming Data - Implementing Security Requirements - Data Protection - Encryption Techniques - Data Governance and Compliance Controls 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🤍 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
In this video I talk about why Apache Spark's in memory processing. That's why Spark is so much faster than Mapreduce or other analytics frameworks. it's simple but awesome for stream processing and batch processing. That's why I explain first what stream and batch processing is. ►Learn Data Engineering with my Data Engineering Academy: 🤍 Check out my free 100+ pages data engineering cookbook on GitHub: 🤍 Please SUPPORT WHAT YOU LIKE: - As an Amazon Associate I earn from qualifying purchases from Amazon. Just use this link: 🤍 #ApacheSpark #DataEngineering #PlumbersofDataScience #bigdata
Hive Bucket End to End #apachehive #hivepartition #hivebucket #hive Big Data Integration Book - 🤍 Video Playlist - Big Data Full Course English - 🤍 Big Data Full Course Tamil - 🤍 Big Data Shorts in Tamil - 🤍 Big Data Shorts in English - 🤍 Hadoop in Tamil - 🤍 Hadoop in English - 🤍 Spark in Tamil - 🤍 Spark in English - 🤍 Hive in Tamil - 🤍 Hive in English - 🤍 NOSQL in English - 🤍 NOSQL in Tamil - 🤍 Scala in Tamil : 🤍 Scala in English: 🤍 Email: atozknowledge.com🤍gmail.com LinkedIn : 🤍 Instagram: 🤍 YouTube channel link 🤍youtube.com/atozknowledgevideos Website 🤍 🤍 Technology in Tamil & English
🔥Edureka Big Data Hadoop Certification Training: 🤍 This Edureka video on "Hive Tutorial" will provide you with detailed knowledge about Hive and the functionalities it can perform. Below are the topics covered in this Hive Tutorial: Why we needed Hive? What is Hive? Features of hive Hive Architecture Hive Components Install Hive Hive Datatypes Hive Operators Hive Data Models Hive Demo 🔹Check our complete Hadoop Blog Series: 🤍 🔹Check our complete Hadoop playlist here: 🤍 To subscribe to our channel and hit the bell icon to never miss an update from us in the future: 🤍 Edureka Community: 🤍 Big Data Podcast - 🤍 Instagram: 🤍 Slideshare: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 #edureka #hadoopedureka #hive #clouderahive #hadoop #bigdata #hadooptutorial #bigdatatraining About the Course: Edureka's Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab. What are the objectives of our Big Data Hadoop Online Course? Big Data Hadoop Certification Training is designed by industry experts to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers: In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS The exposure to many real-world industry-based projects which will be executed in Edureka’s CloudLab Projects which are diverse in nature covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce Rigorous involvement of a Hadoop expert throughout the Big Data Hadoop Training to learn industry standards and best practices What are the skills that you will be learning with our Big Data Hadoop Certification Training? Big Data Hadoop Certification Training will help you to become a Big Data expert. It will hone your skills by offering you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. During Big Data & Hadoop course you will be trained by our expert instructors to: Master the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand how to work with Hadoop storage & resource management. Understand MapReduce Framework Implement complex business solution using MapReduce Learn data ingestion techniques using Sqoop and Flume Perform ETL operations & data analytics using Pig and Hive Implementing Partitioning, Bucketing and Indexing in Hive Understand HBase, i.e a NoSQL Database in Hadoop, HBase Architecture & Mechanisms Integrate HBase with Hive Schedule jobs using Oozie Implement best practices for Hadoop development Understand Apache Spark and its Ecosystem Learn how to work with RDD in Apache Spark Work on real-world Big Data Analytics Project Work on a real-time Hadoop cluster How will Big Data and Hadoop Training help your career? The below predictions will help you in understanding the growth of Big Data: Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts Average Salary of Big Data Hadoop Developers is $97k For more information, please write back to us at sales🤍edureka.in or call us at: IND: 9606058406 / US: 18338555775 (toll free)
На сегодняшний день, инструменты Оркестрации - это отраслевой стандарт для организации получения, обработки и сохранения данных из сотен и даже тысяч разнородных источников с разнообразной частотой обновления и природой. Сценарии использования платформ Оркестрации разнообразны: вы можете просто организовать регулярные select - group by - insert из production базы данных в "холодную" реплику под аналитику, а можете написать целый сервис, который будет раз в час обновлять данные, дообучать ML-модель и поставлять актуальные прогнозные значения конечным пользователям. Данная технология - неотъемлемый инструмент в арсенале современного Data Engineer и администратора вычислительного кластера. На Открытом Уроке мы подробно разберем, что же такое платформы Оркестрации, какие решения есть сегодня на рынке и даже углубимся в практический пример использования одной из самых распространенных платформ на сегодня: Apache Airflow. Приходите, будет интересно! «Экосистема Hadoop, Spark, Hive» - 🤍 Преподаватель: Максим Мигутин – более 5-ти лет опыта в индустрии Данных & Аналитики в роли внешнего косультанта (IBM) и in-house лидера Data Engineering и Data Science-проектов Подключайтесь к обсуждению в чате - 🤍 Пройдите опрос по итогам мероприятия - 🤍 Следите за новостями проекта: - Facebook: 🤍 - Telegram: 🤍 - ВКонтакте: 🤍 - LinkedIn: 🤍 - Хабр: 🤍
Hive Internal Vs External Table #apachehive #hiveacid #hadoop Big Data Integration Book - 🤍 Video Playlist - Big Data Full Course English - 🤍 Big Data Full Course Tamil - 🤍 Big Data Shorts in Tamil - 🤍 Big Data Shorts in English - 🤍 Hadoop in Tamil - 🤍 Hadoop in English - 🤍 Spark in Tamil - 🤍 Spark in English - 🤍 Hive in Tamil - 🤍 Hive in English - 🤍 NOSQL in English - 🤍 NOSQL in Tamil - 🤍 Scala in Tamil : 🤍 Scala in English: 🤍 Email: atozknowledge.com🤍gmail.com LinkedIn : 🤍 Instagram: 🤍 YouTube channel link 🤍youtube.com/atozknowledgevideos Website 🤍 🤍 Technology in Tamil & English
Let's explore the differences between Hive, Hive LLAP and Impala! Hive vs Impala: 🤍 Hive vs Spark SQL: 🤍 Let’s come together in Joining our strong 3500+ 𝐦𝐞𝐦𝐛𝐞𝐫𝐬 community where we impart our knowledge regularly on Data, ML, AI, and many more technologies: 🤍 𝐒𝐭𝐚𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐮𝐬! 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: 🤍 𝐓𝐰𝐢𝐭𝐭𝐞𝐫: 🤍 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: 🤍 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: 🤍 𝐌𝐞𝐝𝐢𝐮𝐦: 🤍 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐲𝐨𝐮𝐭𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐮𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬: 🤍 Comment, Like, Share and Subscribe to our YouTube Channel! #Hive #Impala #DataCouch #HivevsLLAPvsImpala #HivevsHiveLLAP #HivevsImpala #Differences #Technology #Hadoop #BigData #SQL
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